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On the Necessity of Collaboration in Online Model Selection with Decentralized Data
We consider online model selection with decentralized data over $M$ clients, and study a fundamental problem: the necessity of collaboration.
Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks
To address these challenges, we conduct a thorough theoretical convergence analysis for DFL and derive a convergence bound.
Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels
In FEEL, both mobile devices transmitting model parameters over noisy channels and collecting data in diverse environments pose challenges to the generalization of trained models.
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients
Federated Learning (FL) has surged in prominence due to its capability of collaborative model training without direct data sharing.
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